Simulated Inheritance Datasets for Accurate Label Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods and systems fail to accurately trace and analyze the inheritance of data-inheritance events and their associated data, particularly, the change and inheritance of those events may be traceable by comparing data strings among data instances.
Innovation Solution
A method is disclosed for determining inheritance labels of users based on inheritance datasets, involving generating reference panels, constructing simulated data trees, and training a machine learning model with origin-specific weight parameters to predict and adjust labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to trace and analyze data inheritance, then the process is simpler, but the accuracy of determining inheritance labels is insufficient
Solution Approach 1:
The system performs preliminary actions by generating simulated data trees and simulated inheritance datasets before actual analysis. Reference panels are constructed in advance with known inheritance labels, creating a prepared training environment that enables accurate label determination without requiring complex real-time analysis of raw data
Solution Approach 2:
The system creates simulated inheritance datasets that copy the structure and characteristics of real inheritance data. By generating synthetic data trees with known labels that mirror real-world data patterns, the system enables accurate training and evaluation without directly analyzing complex real data, thereby improving accuracy while managing complexity
2Measurement precision
If simulated data trees and origin-specific weight parameters are used, then the accuracy of inheritance analysis is improved, but the computational complexity increases
Solution Approach 1:
The system changes parameters by introducing origin-specific weight parameters that are optimized through training on simulated data. These parameters adjust the importance of different features based on their origin, improving prediction accuracy while providing a structured approach to parameter optimization that manages computational complexity through focused adjustment rather than exhaustive search
3Measurement precision
If multiple reference panels are generated for multiple data-inheritance origins, then the coverage and accuracy of inheritance analysis is improved, but the data processing complexity increases
Solution Approach 1:
The system segments the inheritance analysis problem by creating separate reference panels for different data-inheritance origins. Each reference panel is specialized for a specific origin type, allowing the system to handle diverse inheritance patterns through modular, origin-specific structures. This segmentation improves accuracy by tailoring analysis to each origin while organizing complexity into manageable, independent units
Data Source
AI summary
Disclosed is a method for determining inheritance labels of users based on inheritance datasets of the users. The method includes generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin and comprising reference-panel datasets representative of the data-inheritance origin. The method constructs a plurality of simulated data trees that are built using the reference-panel datasets that are selected from the plurality of reference panels. The method generates a plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a descendant named entity in one of the simulated data trees. The method trains a machine learning model to determine inheritance labels of an inheritance dataset.